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Seurat中Featureplot的问题

Seurat中Featureplot的问题

作者: 单链结合蛋白 | 来源:发表于2023-12-26 15:57 被阅读0次

    I used Featureplot to get the visualization of my srobj. I found cells were not sorted when setting raster=T. The sercond and third plots seem to be the same.

    #when raster=F
    FeaturePlot(object = srobj,
                features =c('STAT3'),
                order = T,
                min.cutoff = 'q10',max.cutoff = 'q90',raster = F)
    
    image.png
    #when raster=T
    FeaturePlot(object = srobj,
                features =c('STAT3'),
                order = T,
                min.cutoff = 'q10',max.cutoff = 'q90',raster = T)
    
    
    image.png
    #when raster=T and not sort cells.
    FeaturePlot(object = srobj,
                features =c('STAT3'),
                order = F,
                min.cutoff = 'q10',max.cutoff = 'q90',raster = T)
    
    
    image.png

    I have try to sort cell manually as https://github.com/satijalab/seurat/issues/5762. But this is not useful. I have tried in r.4.1.3 and r.4.3.2, and got similar results. I checked code of FeaturePlot function and the only difference is this:

    plot <- if (isTRUE(x = raster)) {
        plot + geom_scattermore(mapping = aes_string(x = dims[1], 
          y = dims[2], color = paste0("`", col.by, "`"), shape = shape.by, 
          alpha = alpha.by), pointsize = pt.size, pixels = raster.dpi)
      }
      else {
        plot + geom_point(mapping = aes_string(x = dims[1], 
          y = dims[2], color = paste0("`", col.by, "`"), shape = shape.by, 
          alpha = alpha.by), size = pt.size)
      }
    

    But I am not sure this codes will or not cause the bugs. Now, I use the following code to generate rastered plots.

    library(ggrastr)
    p1 <- FeaturePlot(object = srobj,
                features =c('STAT3'),
                order = T, min.cutoff = 'q10',max.cutoff = 'q90',raster = F)
    rasterize(p1, layers='Point', dpi=300)
    
    

    session info:

    sessionInfo()
    R version 4.3.2 (2023-10-31)
    Platform: x86_64-conda-linux-gnu (64-bit)
    Running under: Ubuntu 22.04.3 LTS

    Matrix products: default
    BLAS/LAPACK: /cluster/home/shanshenbing/soft/anaconda3/envs/r.4.3.2/lib/libopenblasp-r0.3.24.so; LAPACK version 3.11.0

    locale:
    [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
    [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
    [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
    [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

    time zone: Etc/UTC
    tzcode source: system (glibc)

    attached base packages:
    [1] stats graphics grDevices utils datasets methods base

    other attached packages:
    [1] ggrastr_1.0.2 Seurat_4.4.0 SeuratObject_5.0.0 sp_2.1-1
    [5] ggpubr_0.6.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0
    [9] dplyr_1.1.3 purrr_1.0.2 readr_2.1.4 tidyr_1.3.0
    [13] tibble_3.2.1 tidyverse_2.0.0 ggthemes_4.2.4 ggsci_3.0.0
    [17] ggplot2_3.4.4

    loaded via a namespace (and not attached):
    [1] RColorBrewer_1.1-3 rstudioapi_0.15.0 jsonlite_1.8.7
    [4] magrittr_2.0.3 ggbeeswarm_0.7.2 spatstat.utils_3.0-4
    [7] farver_2.1.1 vctrs_0.6.4 ROCR_1.0-11
    [10] Cairo_1.6-2 spatstat.explore_3.2-5 rstatix_0.7.2
    [13] htmltools_0.5.7 broom_1.0.5 sctransform_0.4.1
    [16] parallelly_1.36.0 KernSmooth_2.23-22 htmlwidgets_1.6.2
    [19] ica_1.0-3 plyr_1.8.9 plotly_4.10.3
    [22] zoo_1.8-12 igraph_1.5.1 mime_0.12
    [25] lifecycle_1.0.3 pkgconfig_2.0.3 Matrix_1.6-1.1
    [28] R6_2.5.1 fastmap_1.1.1 fitdistrplus_1.1-11
    [31] future_1.33.0 shiny_1.7.5.1 digest_0.6.33
    [34] colorspace_2.1-0 patchwork_1.1.3 tensor_1.5
    [37] irlba_2.3.5.1 labeling_0.4.3 progressr_0.14.0
    [40] fansi_1.0.5 spatstat.sparse_3.0-3 timechange_0.2.0
    [43] httr_1.4.7 polyclip_1.10-6 abind_1.4-5
    [46] compiler_4.3.2 withr_2.5.2 backports_1.4.1
    [49] carData_3.0-5 qs_0.25.5 ggsignif_0.6.4
    [52] MASS_7.3-60 tools_4.3.2 vipor_0.4.7
    [55] lmtest_0.9-40 beeswarm_0.4.0 httpuv_1.6.12
    [58] future.apply_1.11.0 goftest_1.2-3 glue_1.6.2
    [61] nlme_3.1-163 promises_1.2.1 grid_4.3.2
    [64] Rtsne_0.16 cluster_2.1.4 reshape2_1.4.4
    [67] generics_0.1.3 gtable_0.3.4 spatstat.data_3.0-3
    [70] tzdb_0.4.0 data.table_1.14.8 RApiSerialize_0.1.2
    [73] hms_1.1.3 stringfish_0.15.7 car_3.1-2
    [76] utf8_1.2.4 spatstat.geom_3.2-7 RcppAnnoy_0.0.21
    [79] ggrepel_0.9.4 RANN_2.6.1 pillar_1.9.0
    [82] spam_2.10-0 later_1.3.1 splines_4.3.2
    [85] lattice_0.22-5 survival_3.5-7 deldir_1.0-9
    [88] tidyselect_1.2.0 miniUI_0.1.1.1 pbapply_1.7-2
    [91] gridExtra_2.3 scattermore_1.2 matrixStats_1.0.0
    [94] pheatmap_1.0.12 stringi_1.7.12 lazyeval_0.2.2
    [97] codetools_0.2-19 cli_3.6.1 uwot_0.1.16
    [100] RcppParallel_5.1.6 xtable_1.8-4 reticulate_1.34.0
    [103] munsell_0.5.0 Rcpp_1.0.11 globals_0.16.2
    [106] spatstat.random_3.2-1 png_0.1-8 parallel_4.3.2
    [109] ellipsis_0.3.2 dotCall64_1.1-0 listenv_0.9.0
    [112] viridisLite_0.4.2 scales_1.2.1 ggridges_0.5.4
    [115] leiden_0.4.3 rlang_1.1.2 cowplot_1.1.1

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